This paper examines how trust is constructed, eroded and rebuilt in collaborative complex system design in safety-critical settings.
The paper uses a qualitative process study of a 13-partner European project developing mid-air collision avoidance systems (MIDCAS) for remotely piloted aircraft. Drawing on project documentation, practitioner observation, retrospective interviews and secondary material, the paper develops and applies a four-dimensional analytical framework integrating first- and second-order cybernetics with trust research.
Trust emerged as a dynamic governance achievement shaped by four interrelated dimensions: the balance between trust and control, trust asymmetry in heterogeneous consortia, feedback architecture and iterative trust calibration, and the rebuilding of trust at technical and organisational step-changes. Trust did not substitute for control but enabled viable coordination when formal governance could not absorb project complexity.
The single-case design in a European defence aviation context limits generalisability, but the cybernetic framework offers transferable concepts for examining how trust, control, feedback and vulnerability co-evolve in other collaborative complex system design settings, including artificial intelligence (AI) enabled, autonomous and safety-critical socio-technical systems.
Project managers and funders should treat governance design, feedback architecture, demonstrators and review points as active trust interventions that make evolving vulnerabilities salient, recalibrate the balance between trust and control, and support renewed trust-building at major technical and organisational step-changes.
The paper contributes a cybernetic explanation of trust in collaborative complex system design by linking first-order concerns with feedback, control and requisite variety to second-order concerns with observation, interpretation and meaning-making. It develops an analytical framework for examining trust dynamics and shows how inter-partner trust and trust in the emerging system co-evolve over time, revealing vulnerability as a condition of complexity that can be distributed and managed but never eliminated.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.